Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations768
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.1 KiB
Average record size in memory72.2 B

Variable types

Numeric8
Categorical1

Alerts

Age is highly overall correlated with PregnanciesHigh correlation
BMI is highly overall correlated with SkinThicknessHigh correlation
Pregnancies is highly overall correlated with AgeHigh correlation
SkinThickness is highly overall correlated with BMIHigh correlation
Pregnancies has 111 (14.5%) zeros Zeros

Reproduction

Analysis started2025-08-19 15:08:55.115770
Analysis finished2025-08-19 15:09:08.134301
Duration13.02 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Pregnancies
Real number (ℝ)

High correlation  Zeros 

Distinct15
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8372396
Minimum0
Maximum13.5
Zeros111
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-08-19T15:09:08.224620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum13.5
Range13.5
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3441566
Coefficient of variation (CV)0.8715006
Kurtosis-0.070852832
Mean3.8372396
Median Absolute Deviation (MAD)2
Skewness0.85396175
Sum2947
Variance11.183383
MonotonicityNot monotonic
2025-08-19T15:09:08.337629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 135
17.6%
0 111
14.5%
2 103
13.4%
3 75
9.8%
4 68
8.9%
5 57
7.4%
6 50
 
6.5%
7 45
 
5.9%
8 38
 
4.9%
9 28
 
3.6%
Other values (5) 58
7.6%
ValueCountFrequency (%)
0 111
14.5%
1 135
17.6%
2 103
13.4%
3 75
9.8%
4 68
8.9%
5 57
7.4%
6 50
 
6.5%
7 45
 
5.9%
8 38
 
4.9%
9 28
 
3.6%
ValueCountFrequency (%)
13.5 4
 
0.5%
13 10
 
1.3%
12 9
 
1.2%
11 11
 
1.4%
10 24
3.1%
9 28
3.6%
8 38
4.9%
7 45
5.9%
6 50
6.5%
5 57
7.4%

Glucose
Real number (ℝ)

Distinct136
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.68676
Minimum44
Maximum199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-08-19T15:09:08.465758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile80
Q199.75
median117
Q3140.25
95-th percentile181
Maximum199
Range155
Interquartile range (IQR)40.5

Descriptive statistics

Standard deviation30.435949
Coefficient of variation (CV)0.25011717
Kurtosis-0.2591586
Mean121.68676
Median Absolute Deviation (MAD)20
Skewness0.53271866
Sum93455.434
Variance926.34698
MonotonicityNot monotonic
2025-08-19T15:09:08.622361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 17
 
2.2%
100 17
 
2.2%
111 14
 
1.8%
125 14
 
1.8%
129 14
 
1.8%
106 14
 
1.8%
102 13
 
1.7%
105 13
 
1.7%
112 13
 
1.7%
95 13
 
1.7%
Other values (126) 626
81.5%
ValueCountFrequency (%)
44 1
 
0.1%
56 1
 
0.1%
57 2
0.3%
61 1
 
0.1%
62 1
 
0.1%
65 1
 
0.1%
67 1
 
0.1%
68 3
0.4%
71 4
0.5%
72 1
 
0.1%
ValueCountFrequency (%)
199 1
 
0.1%
198 1
 
0.1%
197 4
0.5%
196 3
0.4%
195 2
0.3%
194 3
0.4%
193 2
0.3%
191 1
 
0.1%
190 1
 
0.1%
189 4
0.5%

BloodPressure
Real number (ℝ)

Distinct39
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.376538
Minimum40
Maximum104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-08-19T15:09:08.754239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile52
Q164
median72.202592
Q380
95-th percentile90
Maximum104
Range64
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.696836
Coefficient of variation (CV)0.16161088
Kurtosis0.21190099
Mean72.376538
Median Absolute Deviation (MAD)7.7974079
Skewness0.10092506
Sum55585.181
Variance136.81598
MonotonicityNot monotonic
2025-08-19T15:09:08.891104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
70 57
 
7.4%
74 52
 
6.8%
68 45
 
5.9%
78 45
 
5.9%
72 44
 
5.7%
64 43
 
5.6%
80 40
 
5.2%
76 39
 
5.1%
60 37
 
4.8%
72.40518417 35
 
4.6%
Other values (29) 331
43.1%
ValueCountFrequency (%)
40 5
 
0.7%
44 4
 
0.5%
46 2
 
0.3%
48 5
 
0.7%
50 13
1.7%
52 11
1.4%
54 11
1.4%
55 2
 
0.3%
56 12
1.6%
58 21
2.7%
ValueCountFrequency (%)
104 12
1.6%
102 1
 
0.1%
100 3
 
0.4%
98 3
 
0.4%
96 4
 
0.5%
95 1
 
0.1%
94 6
 
0.8%
92 8
 
1.0%
90 22
2.9%
88 25
3.3%

SkinThickness
Real number (ℝ)

High correlation 

Distinct38
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.116536
Minimum9.5
Maximum45.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-08-19T15:09:09.016504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.5
5-th percentile14.35
Q123
median23
Q332
95-th percentile44
Maximum45.5
Range36
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.4474231
Coefficient of variation (CV)0.31152294
Kurtosis-0.33115352
Mean27.116536
Median Absolute Deviation (MAD)5
Skewness0.49766495
Sum20825.5
Variance71.358957
MonotonicityNot monotonic
2025-08-19T15:09:09.143747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
23 249
32.4%
32 31
 
4.0%
45.5 31
 
4.0%
30 27
 
3.5%
27 23
 
3.0%
33 20
 
2.6%
28 20
 
2.6%
18 20
 
2.6%
31 19
 
2.5%
19 18
 
2.3%
Other values (28) 310
40.4%
ValueCountFrequency (%)
9.5 4
 
0.5%
10 5
 
0.7%
11 6
 
0.8%
12 7
 
0.9%
13 11
1.4%
14 6
 
0.8%
15 14
1.8%
16 6
 
0.8%
17 14
1.8%
18 20
2.6%
ValueCountFrequency (%)
45.5 31
4.0%
45 6
 
0.8%
44 5
 
0.7%
43 6
 
0.8%
42 11
 
1.4%
41 15
2.0%
40 16
2.1%
39 18
2.3%
38 7
 
0.9%
37 16
2.1%

Insulin
Real number (ℝ)

Distinct186
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean320.36719
Minimum14
Maximum846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-08-19T15:09:09.287920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile50
Q1121.5
median494
Q3494
95-th percentile494
Maximum846
Range832
Interquartile range (IQR)372.5

Descriptive statistics

Standard deviation189.43041
Coefficient of variation (CV)0.59129155
Kurtosis-1.6625201
Mean320.36719
Median Absolute Deviation (MAD)47.5
Skewness-0.21697078
Sum246042
Variance35883.881
MonotonicityNot monotonic
2025-08-19T15:09:09.437165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
494 374
48.7%
105 11
 
1.4%
130 9
 
1.2%
140 9
 
1.2%
120 8
 
1.0%
94 7
 
0.9%
180 7
 
0.9%
100 7
 
0.9%
110 6
 
0.8%
115 6
 
0.8%
Other values (176) 324
42.2%
ValueCountFrequency (%)
14 1
 
0.1%
15 1
 
0.1%
16 1
 
0.1%
18 2
0.3%
22 1
 
0.1%
23 2
0.3%
25 1
 
0.1%
29 1
 
0.1%
32 1
 
0.1%
36 3
0.4%
ValueCountFrequency (%)
846 1
0.1%
744 1
0.1%
680 1
0.1%
600 1
0.1%
579 1
0.1%
545 1
0.1%
543 1
0.1%
540 1
0.1%
510 1
0.1%
495 2
0.3%

BMI
Real number (ℝ)

High correlation 

Distinct241
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.393359
Minimum18.2
Maximum50.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-08-19T15:09:09.572954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18.2
5-th percentile22.235
Q127.5
median32.3
Q336.6
95-th percentile44.395
Maximum50.25
Range32.05
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation6.6674711
Coefficient of variation (CV)0.20582833
Kurtosis-0.19955998
Mean32.393359
Median Absolute Deviation (MAD)4.6
Skewness0.34988209
Sum24878.1
Variance44.455171
MonotonicityNot monotonic
2025-08-19T15:09:09.714872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.3 14
 
1.8%
32 13
 
1.7%
31.6 12
 
1.6%
31.2 12
 
1.6%
33.3 10
 
1.3%
32.4 10
 
1.3%
30.1 9
 
1.2%
32.9 9
 
1.2%
32.8 9
 
1.2%
30.8 9
 
1.2%
Other values (231) 661
86.1%
ValueCountFrequency (%)
18.2 3
0.4%
18.4 1
 
0.1%
19.1 1
 
0.1%
19.3 1
 
0.1%
19.4 1
 
0.1%
19.5 2
0.3%
19.6 3
0.4%
19.9 1
 
0.1%
20 1
 
0.1%
20.1 1
 
0.1%
ValueCountFrequency (%)
50.25 8
1.0%
50 1
 
0.1%
49.7 1
 
0.1%
49.6 1
 
0.1%
49.3 1
 
0.1%
48.8 1
 
0.1%
48.3 1
 
0.1%
47.9 2
 
0.3%
46.8 2
 
0.3%
46.7 1
 
0.1%

DiabetesPedigreeFunction
Real number (ℝ)

Distinct490
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45891406
Minimum0.078
Maximum1.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-08-19T15:09:09.869793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.14035
Q10.24375
median0.3725
Q30.62625
95-th percentile1.13285
Maximum1.2
Range1.122
Interquartile range (IQR)0.3825

Descriptive statistics

Standard deviation0.28559632
Coefficient of variation (CV)0.62233072
Kurtosis0.29744677
Mean0.45891406
Median Absolute Deviation (MAD)0.1675
Skewness1.0244278
Sum352.446
Variance0.081565257
MonotonicityNot monotonic
2025-08-19T15:09:10.010433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 29
 
3.8%
0.258 6
 
0.8%
0.254 6
 
0.8%
0.207 5
 
0.7%
0.268 5
 
0.7%
0.238 5
 
0.7%
0.259 5
 
0.7%
0.261 5
 
0.7%
0.304 4
 
0.5%
0.27 4
 
0.5%
Other values (480) 694
90.4%
ValueCountFrequency (%)
0.078 1
0.1%
0.084 1
0.1%
0.085 2
0.3%
0.088 2
0.3%
0.089 1
0.1%
0.092 1
0.1%
0.096 1
0.1%
0.1 1
0.1%
0.101 1
0.1%
0.102 1
0.1%
ValueCountFrequency (%)
1.2 29
3.8%
1.191 1
 
0.1%
1.189 1
 
0.1%
1.182 1
 
0.1%
1.174 1
 
0.1%
1.162 1
 
0.1%
1.159 1
 
0.1%
1.154 1
 
0.1%
1.144 1
 
0.1%
1.138 1
 
0.1%

Age
Real number (ℝ)

High correlation 

Distinct47
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.19987
Minimum21
Maximum66.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-08-19T15:09:10.138160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q341
95-th percentile58
Maximum66.5
Range45.5
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.628404
Coefficient of variation (CV)0.3502545
Kurtosis0.33096993
Mean33.19987
Median Absolute Deviation (MAD)7
Skewness1.0671703
Sum25497.5
Variance135.21978
MonotonicityNot monotonic
2025-08-19T15:09:10.281728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
22 72
 
9.4%
21 63
 
8.2%
25 48
 
6.2%
24 46
 
6.0%
23 38
 
4.9%
28 35
 
4.6%
26 33
 
4.3%
27 32
 
4.2%
29 29
 
3.8%
31 24
 
3.1%
Other values (37) 348
45.3%
ValueCountFrequency (%)
21 63
8.2%
22 72
9.4%
23 38
4.9%
24 46
6.0%
25 48
6.2%
26 33
4.3%
27 32
4.2%
28 35
4.6%
29 29
3.8%
30 21
 
2.7%
ValueCountFrequency (%)
66.5 9
1.2%
66 4
0.5%
65 3
 
0.4%
64 1
 
0.1%
63 4
0.5%
62 4
0.5%
61 2
 
0.3%
60 5
0.7%
59 3
 
0.4%
58 7
0.9%

Outcome
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
0.0
500 
1.0
268 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 500
65.1%
1.0 268
34.9%

Length

2025-08-19T15:09:10.402821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T15:09:10.465031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 500
65.1%
1.0 268
34.9%

Most occurring characters

ValueCountFrequency (%)
0 1268
55.0%
. 768
33.3%
1 268
 
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1536
66.7%
Other Punctuation 768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1268
82.6%
1 268
 
17.4%
Other Punctuation
ValueCountFrequency (%)
. 768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1268
55.0%
. 768
33.3%
1 268
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1268
55.0%
. 768
33.3%
1 268
 
11.6%

Interactions

2025-08-19T15:09:05.148142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:55.373466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:56.276703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:58.788569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:59.845108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:00.913929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:01.916256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:03.114277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:05.370523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:55.486580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:56.842797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:58.933835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:59.983066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:01.031426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:02.074729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:03.303591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:05.587979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:55.598962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:57.421086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:59.092302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:00.137755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:01.179650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:02.247097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:03.639027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:05.766744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:55.716633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:57.958208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:59.233413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:00.278586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:01.313166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:02.398506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:03.861138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:05.988670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:55.829043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:58.181428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:59.367483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:00.415972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:01.430826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:02.542497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:04.201918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:06.233028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:55.927978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:58.354674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:59.481534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:00.541490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:01.535255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:02.682738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:04.456682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:07.686710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:56.047492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:58.495375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:59.603311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:00.662016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:01.660045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:02.829238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:04.696342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:07.802642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:56.163368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:58.649705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:08:59.728596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:00.790934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:01.785814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:02.976910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T15:09:04.881666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-19T15:09:10.524594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeBMIBloodPressureDiabetesPedigreeFunctionGlucoseInsulinOutcomePregnanciesSkinThickness
Age1.0000.1210.3640.0430.2810.2660.3290.6070.115
BMI0.1211.0000.2900.1340.2250.0120.3270.0010.571
BloodPressure0.3640.2901.0000.0080.2420.1580.1610.1870.162
DiabetesPedigreeFunction0.0430.1340.0081.0000.090-0.1440.168-0.0430.109
Glucose0.2810.2250.2420.0901.0000.2020.4790.1290.161
Insulin0.2660.0120.158-0.1440.2021.0000.2410.193-0.081
Outcome0.3290.3270.1610.1680.4790.2411.0000.2480.202
Pregnancies0.6070.0010.187-0.0430.1290.1930.2481.0000.038
SkinThickness0.1150.5710.1620.1090.161-0.0810.2020.0381.000

Missing values

2025-08-19T15:09:07.945770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-19T15:09:08.052094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
06.0148.072.00000035.0494.033.60.62750.01.0
11.085.066.00000029.0494.026.60.35131.00.0
28.0183.064.00000023.0494.023.30.67232.01.0
31.089.066.00000023.094.028.10.16721.00.0
40.0137.040.00000035.0168.043.11.20033.01.0
55.0116.074.00000023.0494.025.60.20130.00.0
63.078.050.00000032.088.031.00.24826.01.0
710.0115.072.40518423.0494.035.30.13429.00.0
82.0197.070.00000045.0543.030.50.15853.01.0
98.0125.096.00000023.0494.032.30.23254.01.0
PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
7581.0106.076.023.0494.037.50.19726.00.0
7596.0190.092.023.0494.035.50.27866.01.0
7602.088.058.026.016.028.40.76622.00.0
7619.0170.074.031.0494.044.00.40343.01.0
7629.089.062.023.0494.022.50.14233.00.0
76310.0101.076.045.5180.032.90.17163.00.0
7642.0122.070.027.0494.036.80.34027.00.0
7655.0121.072.023.0112.026.20.24530.00.0
7661.0126.060.023.0494.030.10.34947.01.0
7671.093.070.031.0494.030.40.31523.00.0